Overview

Dataset statistics

Number of variables17
Number of observations896
Missing cells1134
Missing cells (%)7.4%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory268.9 KiB
Average record size in memory307.3 B

Variable types

NUM12
CAT4
UNSUPPORTED1

Warnings

Category has constant value "896" Constant
Year has constant value "896" Constant
DBN has a high cardinality: 216 distinct values High cardinality
Level 2# is highly correlated with Students TestedHigh correlation
Students Tested is highly correlated with Level 2#High correlation
Level 3 and 4# is highly correlated with Level 3#High correlation
Level 3# is highly correlated with Level 3 and 4#High correlation
Level 3 and 4% is highly correlated with Mean Scale Score and 1 other fieldsHigh correlation
Mean Scale Score is highly correlated with Level 3 and 4%High correlation
Level 3% is highly correlated with Level 3 and 4%High correlation
NYC Results on the New York State 2006 Math Test (Grades 3-8) has 896 (100.0%) missing values Missing
Mean Scale Score has 218 (24.3%) missing values Missing
DBN is uniformly distributed Uniform
NYC Results on the New York State 2006 Math Test (Grades 3-8) is an unsupported type, check if it needs cleaning or further analysis Unsupported
Level 1# has 19 (2.1%) zeros Zeros
Level 1% has 19 (2.1%) zeros Zeros
Level 4# has 75 (8.4%) zeros Zeros
Level 4% has 75 (8.4%) zeros Zeros

Reproduction

Analysis started2020-12-12 21:53:30.692810
Analysis finished2020-12-12 21:53:44.814962
Duration14.12 seconds
Software versionpandas-profiling v2.9.0
Download configurationconfig.yaml

Variables

Missing896
Missing (%)100.0%
Memory size7.1 KiB

DBN
Categorical

HIGH CARDINALITY
UNIFORM

Distinct216
Distinct (%)24.1%
Missing0
Missing (%)0.0%
Memory size7.1 KiB
11X083
 
7
10X315
 
7
10X095
 
7
09X004
 
7
11X194
 
7
Other values (211)
861 
ValueCountFrequency (%) 
11X08370.8%
 
10X31570.8%
 
10X09570.8%
 
09X00470.8%
 
11X19470.8%
 
12X21170.8%
 
11X08970.8%
 
10X01570.8%
 
12X21470.8%
 
10X00370.8%
 
10X27970.8%
 
11X17570.8%
 
11X01970.8%
 
10X02070.8%
 
10X03770.8%
 
09X21870.8%
 
12X21270.8%
 
07X02960.7%
 
10X28060.7%
 
07X02560.7%
 
07X03160.7%
 
10X30660.7%
 
08X07150.6%
 
12X13450.6%
 
08X09350.6%
 
Other values (191)73281.7%
 
2020-12-12T16:53:44.885023image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Frequencies of value counts

Unique

Unique0 ?
Unique (%)0.0%
2020-12-12T16:53:44.955583image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram of lengths of the category

Length

Max length6
Median length6
Mean length6
Min length6

Overview of Unicode Properties

Unique unicode characters11
Unique unicode categories2 ?
Unique unicode scripts2 ?
Unique unicode blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Most occurring characters

ValueCountFrequency (%) 
0121322.6%
 
1112320.9%
 
X89616.7%
 
24879.1%
 
93666.8%
 
32755.1%
 
82594.8%
 
72404.5%
 
51873.5%
 
41673.1%
 
61633.0%
 

Most occurring categories

ValueCountFrequency (%) 
Decimal Number448083.3%
 
Uppercase Letter89616.7%
 

Most frequent Decimal Number characters

ValueCountFrequency (%) 
0121327.1%
 
1112325.1%
 
248710.9%
 
93668.2%
 
32756.1%
 
82595.8%
 
72405.4%
 
51874.2%
 
41673.7%
 
61633.6%
 

Most frequent Uppercase Letter characters

ValueCountFrequency (%) 
X896100.0%
 

Most occurring scripts

ValueCountFrequency (%) 
Common448083.3%
 
Latin89616.7%
 

Most frequent Common characters

ValueCountFrequency (%) 
0121327.1%
 
1112325.1%
 
248710.9%
 
93668.2%
 
32756.1%
 
82595.8%
 
72405.4%
 
51874.2%
 
41673.7%
 
61633.6%
 

Most frequent Latin characters

ValueCountFrequency (%) 
X896100.0%
 

Most occurring blocks

ValueCountFrequency (%) 
ASCII5376100.0%
 

Most frequent ASCII characters

ValueCountFrequency (%) 
0121322.6%
 
1112320.9%
 
X89616.7%
 
24879.1%
 
93666.8%
 
32755.1%
 
82594.8%
 
72404.5%
 
51873.5%
 
41673.1%
 
61633.0%
 

Grade
Categorical

Distinct7
Distinct (%)0.8%
Missing0
Missing (%)0.0%
Memory size7.1 KiB
All Grades
216 
3
140 
4
139 
5
132 
6
110 
Other values (2)
159 
ValueCountFrequency (%) 
All Grades21624.1%
 
314015.6%
 
413915.5%
 
513214.7%
 
611012.3%
 
7889.8%
 
8717.9%
 
2020-12-12T16:53:45.020639image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Frequencies of value counts

Unique

Unique0 ?
Unique (%)0.0%
2020-12-12T16:53:45.070182image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-12T16:53:45.131235image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram of lengths of the category

Length

Max length10
Median length1
Mean length3.169642857
Min length1

Overview of Unicode Properties

Unique unicode characters15
Unique unicode categories4 ?
Unique unicode scripts2 ?
Unique unicode blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Most occurring characters

ValueCountFrequency (%) 
l43215.2%
 
A2167.6%
 
2167.6%
 
G2167.6%
 
r2167.6%
 
a2167.6%
 
d2167.6%
 
e2167.6%
 
s2167.6%
 
31404.9%
 
41394.9%
 
51324.6%
 
61103.9%
 
7883.1%
 
8712.5%
 

Most occurring categories

ValueCountFrequency (%) 
Lowercase Letter151253.2%
 
Decimal Number68023.9%
 
Uppercase Letter43215.2%
 
Space Separator2167.6%
 

Most frequent Decimal Number characters

ValueCountFrequency (%) 
314020.6%
 
413920.4%
 
513219.4%
 
611016.2%
 
78812.9%
 
87110.4%
 

Most frequent Uppercase Letter characters

ValueCountFrequency (%) 
A21650.0%
 
G21650.0%
 

Most frequent Lowercase Letter characters

ValueCountFrequency (%) 
l43228.6%
 
r21614.3%
 
a21614.3%
 
d21614.3%
 
e21614.3%
 
s21614.3%
 

Most frequent Space Separator characters

ValueCountFrequency (%) 
216100.0%
 

Most occurring scripts

ValueCountFrequency (%) 
Latin194468.5%
 
Common89631.5%
 

Most frequent Common characters

ValueCountFrequency (%) 
21624.1%
 
314015.6%
 
413915.5%
 
513214.7%
 
611012.3%
 
7889.8%
 
8717.9%
 

Most frequent Latin characters

ValueCountFrequency (%) 
l43222.2%
 
A21611.1%
 
G21611.1%
 
r21611.1%
 
a21611.1%
 
d21611.1%
 
e21611.1%
 
s21611.1%
 

Most occurring blocks

ValueCountFrequency (%) 
ASCII2840100.0%
 

Most frequent ASCII characters

ValueCountFrequency (%) 
l43215.2%
 
A2167.6%
 
2167.6%
 
G2167.6%
 
r2167.6%
 
a2167.6%
 
d2167.6%
 
e2167.6%
 
s2167.6%
 
31404.9%
 
41394.9%
 
51324.6%
 
61103.9%
 
7883.1%
 
8712.5%
 

Category
Categorical

CONSTANT
REJECTED

Distinct1
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size7.1 KiB
All Students
896 
ValueCountFrequency (%) 
All Students896100.0%
 
2020-12-12T16:53:45.189785image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Frequencies of value counts

Unique

Unique0 ?
Unique (%)0.0%
2020-12-12T16:53:45.228818image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-12T16:53:45.266851image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram of lengths of the category

Length

Max length12
Median length12
Mean length12
Min length12

Overview of Unicode Properties

Unique unicode characters10
Unique unicode categories3 ?
Unique unicode scripts2 ?
Unique unicode blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Most occurring characters

ValueCountFrequency (%) 
l179216.7%
 
t179216.7%
 
A8968.3%
 
8968.3%
 
S8968.3%
 
u8968.3%
 
d8968.3%
 
e8968.3%
 
n8968.3%
 
s8968.3%
 

Most occurring categories

ValueCountFrequency (%) 
Lowercase Letter806475.0%
 
Uppercase Letter179216.7%
 
Space Separator8968.3%
 

Most frequent Uppercase Letter characters

ValueCountFrequency (%) 
A89650.0%
 
S89650.0%
 

Most frequent Lowercase Letter characters

ValueCountFrequency (%) 
l179222.2%
 
t179222.2%
 
u89611.1%
 
d89611.1%
 
e89611.1%
 
n89611.1%
 
s89611.1%
 

Most frequent Space Separator characters

ValueCountFrequency (%) 
896100.0%
 

Most occurring scripts

ValueCountFrequency (%) 
Latin985691.7%
 
Common8968.3%
 

Most frequent Latin characters

ValueCountFrequency (%) 
l179218.2%
 
t179218.2%
 
A8969.1%
 
S8969.1%
 
u8969.1%
 
d8969.1%
 
e8969.1%
 
n8969.1%
 
s8969.1%
 

Most frequent Common characters

ValueCountFrequency (%) 
896100.0%
 

Most occurring blocks

ValueCountFrequency (%) 
ASCII10752100.0%
 

Most frequent ASCII characters

ValueCountFrequency (%) 
l179216.7%
 
t179216.7%
 
A8968.3%
 
8968.3%
 
S8968.3%
 
u8968.3%
 
d8968.3%
 
e8968.3%
 
n8968.3%
 
s8968.3%
 

Year
Categorical

CONSTANT
REJECTED

Distinct1
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size7.1 KiB
2006
896 
ValueCountFrequency (%) 
2006896100.0%
 
2020-12-12T16:53:45.324901image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Frequencies of value counts

Unique

Unique0 ?
Unique (%)0.0%
2020-12-12T16:53:45.363435image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-12T16:53:45.400967image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram of lengths of the category

Length

Max length4
Median length4
Mean length4
Min length4

Overview of Unicode Properties

Unique unicode characters3
Unique unicode categories1 ?
Unique unicode scripts1 ?
Unique unicode blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Most occurring characters

ValueCountFrequency (%) 
0179250.0%
 
289625.0%
 
689625.0%
 

Most occurring categories

ValueCountFrequency (%) 
Decimal Number3584100.0%
 

Most frequent Decimal Number characters

ValueCountFrequency (%) 
0179250.0%
 
289625.0%
 
689625.0%
 

Most occurring scripts

ValueCountFrequency (%) 
Common3584100.0%
 

Most frequent Common characters

ValueCountFrequency (%) 
0179250.0%
 
289625.0%
 
689625.0%
 

Most occurring blocks

ValueCountFrequency (%) 
ASCII3584100.0%
 

Most frequent ASCII characters

ValueCountFrequency (%) 
0179250.0%
 
289625.0%
 
689625.0%
 

Mean Scale Score
Real number (ℝ≥0)

HIGH CORRELATION
MISSING

Distinct96
Distinct (%)14.2%
Missing218
Missing (%)24.3%
Infinite0
Infinite (%)0.0%
Mean648.1445428
Minimum593
Maximum699
Zeros0
Zeros (%)0.0%
Memory size7.1 KiB
2020-12-12T16:53:45.468525image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

Quantile statistics

Minimum593
5-th percentile615.85
Q1636
median648
Q3660
95-th percentile681
Maximum699
Range106
Interquartile range (IQR)24

Descriptive statistics

Standard deviation18.79485122
Coefficient of variation (CV)0.02899793176
Kurtosis-0.02096183613
Mean648.1445428
Median Absolute Deviation (MAD)12
Skewness-0.03173929843
Sum439442
Variance353.2464325
MonotocityNot monotonic
2020-12-12T16:53:45.548594image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%) 
655222.5%
 
647192.1%
 
641192.1%
 
653192.1%
 
656192.1%
 
651182.0%
 
645182.0%
 
633161.8%
 
636161.8%
 
657151.7%
 
650141.6%
 
640141.6%
 
637131.5%
 
644131.5%
 
646131.5%
 
643131.5%
 
649121.3%
 
660121.3%
 
661121.3%
 
654121.3%
 
642121.3%
 
662121.3%
 
658121.3%
 
648121.3%
 
664121.3%
 
Other values (71)30934.5%
 
(Missing)21824.3%
 
ValueCountFrequency (%) 
59310.1%
 
59720.2%
 
59810.1%
 
60110.1%
 
60310.1%
 
60410.1%
 
60510.1%
 
60610.1%
 
60720.2%
 
60820.2%
 
ValueCountFrequency (%) 
69910.1%
 
69610.1%
 
69520.2%
 
69410.1%
 
69320.2%
 
69030.3%
 
68810.1%
 
68770.8%
 
68620.2%
 
68520.2%
 

Students Tested
Real number (ℝ≥0)

HIGH CORRELATION

Distinct370
Distinct (%)41.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean200.3169643
Minimum1
Maximum1316
Zeros0
Zeros (%)0.0%
Memory size7.1 KiB
2020-12-12T16:53:45.627161image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile51
Q188
median137
Q3238.25
95-th percentile561.5
Maximum1316
Range1315
Interquartile range (IQR)150.25

Descriptive statistics

Standard deviation189.396914
Coefficient of variation (CV)0.9454861434
Kurtosis9.062007247
Mean200.3169643
Median Absolute Deviation (MAD)59
Skewness2.702765249
Sum179484
Variance35871.19104
MonotocityNot monotonic
2020-12-12T16:53:45.710733image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%) 
88101.1%
 
77101.1%
 
8380.9%
 
9480.9%
 
8980.9%
 
8780.9%
 
6480.9%
 
12470.8%
 
12770.8%
 
7170.8%
 
6670.8%
 
10070.8%
 
15370.8%
 
11570.8%
 
11270.8%
 
12970.8%
 
15770.8%
 
8670.8%
 
9670.8%
 
10870.8%
 
11460.7%
 
5760.7%
 
10960.7%
 
7860.7%
 
10660.7%
 
Other values (345)71579.8%
 
ValueCountFrequency (%) 
110.1%
 
210.1%
 
1620.2%
 
1810.1%
 
1910.1%
 
2320.2%
 
2420.2%
 
2520.2%
 
2820.2%
 
3110.1%
 
ValueCountFrequency (%) 
131610.1%
 
130210.1%
 
122410.1%
 
118610.1%
 
114110.1%
 
113810.1%
 
109410.1%
 
106910.1%
 
105610.1%
 
105110.1%
 

Level 1#
Real number (ℝ≥0)

ZEROS

Distinct159
Distinct (%)17.8%
Missing2
Missing (%)0.2%
Infinite0
Infinite (%)0.0%
Mean40.95302013
Minimum0
Maximum418
Zeros19
Zeros (%)2.1%
Memory size7.1 KiB
2020-12-12T16:53:45.794806image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile2
Q111
median22
Q347
95-th percentile141.35
Maximum418
Range418
Interquartile range (IQR)36

Descriptive statistics

Standard deviation53.82338114
Coefficient of variation (CV)1.314271352
Kurtosis13.16439458
Mean40.95302013
Median Absolute Deviation (MAD)14
Skewness3.193702023
Sum36612
Variance2896.956357
MonotocityNot monotonic
2020-12-12T16:53:45.872873image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%) 
12283.1%
 
9273.0%
 
11262.9%
 
8242.7%
 
16232.6%
 
2222.5%
 
14212.3%
 
22212.3%
 
17192.1%
 
15192.1%
 
18192.1%
 
0192.1%
 
25192.1%
 
13182.0%
 
7182.0%
 
10182.0%
 
20182.0%
 
23171.9%
 
3171.9%
 
21161.8%
 
6161.8%
 
1161.8%
 
28161.8%
 
5161.8%
 
19141.6%
 
Other values (134)40745.4%
 
ValueCountFrequency (%) 
0192.1%
 
1161.8%
 
2222.5%
 
3171.9%
 
4131.5%
 
5161.8%
 
6161.8%
 
7182.0%
 
8242.7%
 
9273.0%
 
ValueCountFrequency (%) 
41810.1%
 
39110.1%
 
38010.1%
 
37010.1%
 
35010.1%
 
32010.1%
 
31210.1%
 
29310.1%
 
28310.1%
 
28110.1%
 

Level 1%
Real number (ℝ≥0)

ZEROS

Distinct356
Distinct (%)39.8%
Missing2
Missing (%)0.2%
Infinite0
Infinite (%)0.0%
Mean18.54317673
Minimum0
Maximum67.5
Zeros19
Zeros (%)2.1%
Memory size7.1 KiB
2020-12-12T16:53:45.954443image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile2.6
Q110.2
median16.8
Q324.1
95-th percentile43.205
Maximum67.5
Range67.5
Interquartile range (IQR)13.9

Descriptive statistics

Standard deviation11.98403162
Coefficient of variation (CV)0.6462771612
Kurtosis1.4415262
Mean18.54317673
Median Absolute Deviation (MAD)6.85
Skewness1.09321953
Sum16577.6
Variance143.6170139
MonotocityNot monotonic
2020-12-12T16:53:46.032010image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%) 
0192.1%
 
9.8111.2%
 
2091.0%
 
9.480.9%
 
18.380.9%
 
15.680.9%
 
17.570.8%
 
21.270.8%
 
14.960.7%
 
16.960.7%
 
11.460.7%
 
1860.7%
 
1760.7%
 
20.560.7%
 
20.260.7%
 
2560.7%
 
1960.7%
 
16.760.7%
 
19.460.7%
 
18.460.7%
 
11.360.7%
 
14.760.7%
 
750.6%
 
2.650.6%
 
3.650.6%
 
Other values (331)71880.1%
 
ValueCountFrequency (%) 
0192.1%
 
0.310.1%
 
0.620.2%
 
110.1%
 
1.120.2%
 
1.310.1%
 
1.430.3%
 
1.710.1%
 
1.830.3%
 
1.910.1%
 
ValueCountFrequency (%) 
67.510.1%
 
66.110.1%
 
61.110.1%
 
58.910.1%
 
57.910.1%
 
57.810.1%
 
57.110.1%
 
56.210.1%
 
56.110.1%
 
5510.1%
 

Level 2#
Real number (ℝ≥0)

HIGH CORRELATION

Distinct203
Distinct (%)22.7%
Missing2
Missing (%)0.2%
Infinite0
Infinite (%)0.0%
Mean65.12304251
Minimum1
Maximum553
Zeros0
Zeros (%)0.0%
Memory size7.1 KiB
2020-12-12T16:53:46.112579image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile8
Q124
median40
Q378
95-th percentile209
Maximum553
Range552
Interquartile range (IQR)54

Descriptive statistics

Standard deviation73.11835552
Coefficient of variation (CV)1.122772412
Kurtosis12.30230511
Mean65.12304251
Median Absolute Deviation (MAD)21
Skewness3.071253461
Sum58220
Variance5346.293914
MonotocityNot monotonic
2020-12-12T16:53:46.188644image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%) 
21283.1%
 
30192.1%
 
22182.0%
 
23171.9%
 
34171.9%
 
32171.9%
 
26161.8%
 
25161.8%
 
31151.7%
 
33151.7%
 
36141.6%
 
29141.6%
 
40141.6%
 
16131.5%
 
38131.5%
 
56131.5%
 
18121.3%
 
28121.3%
 
20121.3%
 
10121.3%
 
15121.3%
 
19121.3%
 
39121.3%
 
7111.2%
 
35111.2%
 
Other values (178)52959.0%
 
ValueCountFrequency (%) 
140.4%
 
210.1%
 
340.4%
 
491.0%
 
560.7%
 
630.3%
 
7111.2%
 
880.9%
 
980.9%
 
10121.3%
 
ValueCountFrequency (%) 
55310.1%
 
51810.1%
 
50610.1%
 
50410.1%
 
49610.1%
 
47110.1%
 
46910.1%
 
43010.1%
 
41010.1%
 
36110.1%
 

Level 2%
Real number (ℝ≥0)

Distinct385
Distinct (%)43.1%
Missing2
Missing (%)0.2%
Infinite0
Infinite (%)0.0%
Mean30.98702461
Minimum2.1
Maximum68.9
Zeros0
Zeros (%)0.0%
Memory size7.1 KiB
2020-12-12T16:53:46.269214image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

Quantile statistics

Minimum2.1
5-th percentile10.295
Q123.225
median31.4
Q339.4
95-th percentile48.8
Maximum68.9
Range66.8
Interquartile range (IQR)16.175

Descriptive statistics

Standard deviation11.39779204
Coefficient of variation (CV)0.3678246682
Kurtosis-0.3195785394
Mean30.98702461
Median Absolute Deviation (MAD)8.05
Skewness-0.166957306
Sum27702.4
Variance129.9096635
MonotocityNot monotonic
2020-12-12T16:53:46.346780image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%) 
33.3101.1%
 
28.6101.1%
 
3680.9%
 
3170.8%
 
37.170.8%
 
37.270.8%
 
32.260.7%
 
2060.7%
 
35.560.7%
 
36.860.7%
 
42.160.7%
 
26.160.7%
 
24.660.7%
 
4060.7%
 
39.860.7%
 
37.460.7%
 
2660.7%
 
2560.7%
 
31.360.7%
 
20.850.6%
 
27.950.6%
 
21.750.6%
 
29.850.6%
 
28.750.6%
 
14.950.6%
 
Other values (360)73782.3%
 
ValueCountFrequency (%) 
2.110.1%
 
2.310.1%
 
2.810.1%
 
3.610.1%
 
4.210.1%
 
4.330.3%
 
4.820.2%
 
5.610.1%
 
5.730.3%
 
5.910.1%
 
ValueCountFrequency (%) 
68.910.1%
 
59.810.1%
 
57.410.1%
 
56.910.1%
 
56.620.2%
 
5610.1%
 
55.810.1%
 
55.510.1%
 
55.110.1%
 
54.510.1%
 

Level 3#
Real number (ℝ≥0)

HIGH CORRELATION

Distinct221
Distinct (%)24.7%
Missing2
Missing (%)0.2%
Infinite0
Infinite (%)0.0%
Mean79.14653244
Minimum0
Maximum559
Zeros1
Zeros (%)0.1%
Memory size7.1 KiB
2020-12-12T16:53:46.427850image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile16
Q135
median55
Q394.75
95-th percentile221.35
Maximum559
Range559
Interquartile range (IQR)59.75

Descriptive statistics

Standard deviation74.61083659
Coefficient of variation (CV)0.9426924249
Kurtosis9.278584537
Mean79.14653244
Median Absolute Deviation (MAD)25
Skewness2.671714058
Sum70757
Variance5566.776936
MonotocityNot monotonic
2020-12-12T16:53:46.502915image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%) 
41171.9%
 
34171.9%
 
38171.9%
 
44171.9%
 
37151.7%
 
30151.7%
 
35141.6%
 
27141.6%
 
29131.5%
 
23131.5%
 
46121.3%
 
42121.3%
 
32121.3%
 
52121.3%
 
45121.3%
 
47121.3%
 
89111.2%
 
36111.2%
 
51111.2%
 
26111.2%
 
25101.1%
 
28101.1%
 
78101.1%
 
50101.1%
 
49101.1%
 
Other values (196)57664.3%
 
ValueCountFrequency (%) 
010.1%
 
110.1%
 
520.2%
 
620.2%
 
750.6%
 
820.2%
 
920.2%
 
1030.3%
 
1150.6%
 
1260.7%
 
ValueCountFrequency (%) 
55910.1%
 
50710.1%
 
50210.1%
 
48210.1%
 
46410.1%
 
44010.1%
 
43610.1%
 
43010.1%
 
42410.1%
 
38910.1%
 

Level 3%
Real number (ℝ≥0)

HIGH CORRELATION

Distinct433
Distinct (%)48.4%
Missing2
Missing (%)0.2%
Infinite0
Infinite (%)0.0%
Mean41.91957494
Minimum0
Maximum81.1
Zeros1
Zeros (%)0.1%
Memory size7.1 KiB
2020-12-12T16:53:46.585486image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile14.365
Q132.6
median44
Q352.3
95-th percentile62.505
Maximum81.1
Range81.1
Interquartile range (IQR)19.7

Descriptive statistics

Standard deviation14.3286019
Coefficient of variation (CV)0.3418117174
Kurtosis-0.1967819867
Mean41.91957494
Median Absolute Deviation (MAD)9.55
Skewness-0.4473878889
Sum37476.1
Variance205.3088325
MonotocityNot monotonic
2020-12-12T16:53:46.665555image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%) 
50101.1%
 
53.791.0%
 
56.380.9%
 
43.670.8%
 
42.960.7%
 
46.360.7%
 
50.660.7%
 
35.260.7%
 
49.560.7%
 
48.560.7%
 
33.360.7%
 
48.960.7%
 
41.760.7%
 
4060.7%
 
45.560.7%
 
40.650.6%
 
41.550.6%
 
42.650.6%
 
45.850.6%
 
52.550.6%
 
46.950.6%
 
42.750.6%
 
47.850.6%
 
45.950.6%
 
4150.6%
 
Other values (408)74483.0%
 
ValueCountFrequency (%) 
010.1%
 
0.910.1%
 
510.1%
 
6.110.1%
 
6.510.1%
 
6.930.3%
 
7.320.2%
 
7.410.1%
 
8.410.1%
 
8.910.1%
 
ValueCountFrequency (%) 
81.110.1%
 
77.110.1%
 
75.810.1%
 
7410.1%
 
73.610.1%
 
73.210.1%
 
73.110.1%
 
7310.1%
 
72.310.1%
 
71.710.1%
 

Level 4#
Real number (ℝ≥0)

ZEROS

Distinct91
Distinct (%)10.2%
Missing2
Missing (%)0.2%
Infinite0
Infinite (%)0.0%
Mean15.53914989
Minimum0
Maximum133
Zeros75
Zeros (%)8.4%
Memory size7.1 KiB
2020-12-12T16:53:46.747125image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q13
median9
Q320
95-th percentile56
Maximum133
Range133
Interquartile range (IQR)17

Descriptive statistics

Standard deviation20.07433358
Coefficient of variation (CV)1.291855328
Kurtosis8.305410535
Mean15.53914989
Median Absolute Deviation (MAD)7
Skewness2.589522562
Sum13892
Variance402.9788687
MonotocityNot monotonic
2020-12-12T16:53:46.831698image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%) 
0758.4%
 
1697.7%
 
2556.1%
 
5546.0%
 
4515.7%
 
3485.4%
 
10364.0%
 
7323.6%
 
9313.5%
 
6303.3%
 
8293.2%
 
12293.2%
 
11232.6%
 
14182.0%
 
20182.0%
 
19171.9%
 
21161.8%
 
15131.5%
 
24131.5%
 
18131.5%
 
13121.3%
 
23111.2%
 
17111.2%
 
27111.2%
 
26101.1%
 
Other values (66)16918.9%
 
ValueCountFrequency (%) 
0758.4%
 
1697.7%
 
2556.1%
 
3485.4%
 
4515.7%
 
5546.0%
 
6303.3%
 
7323.6%
 
8293.2%
 
9313.5%
 
ValueCountFrequency (%) 
13310.1%
 
13110.1%
 
12910.1%
 
11910.1%
 
11210.1%
 
11110.1%
 
10710.1%
 
10410.1%
 
10110.1%
 
10020.2%
 

Level 4%
Real number (ℝ≥0)

ZEROS

Distinct252
Distinct (%)28.2%
Missing2
Missing (%)0.2%
Infinite0
Infinite (%)0.0%
Mean8.553579418
Minimum0
Maximum47.5
Zeros75
Zeros (%)8.4%
Memory size7.1 KiB
2020-12-12T16:53:46.916270image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q12.3
median6.3
Q311.6
95-th percentile26.435
Maximum47.5
Range47.5
Interquartile range (IQR)9.3

Descriptive statistics

Standard deviation8.486097157
Coefficient of variation (CV)0.9921106407
Kurtosis3.105645449
Mean8.553579418
Median Absolute Deviation (MAD)4.4
Skewness1.654015435
Sum7646.9
Variance72.01384496
MonotocityNot monotonic
2020-12-12T16:53:46.994338image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%) 
0758.4%
 
1.2131.5%
 
1.4131.5%
 
1.9121.3%
 
0.8121.3%
 
8.3111.2%
 
2.9111.2%
 
6.5111.2%
 
9.8111.2%
 
5.3101.1%
 
3.4101.1%
 
1.1101.1%
 
1101.1%
 
491.0%
 
691.0%
 
0.680.9%
 
2.380.9%
 
5.480.9%
 
2.780.9%
 
0.780.9%
 
4.580.9%
 
11.480.9%
 
7.780.9%
 
0.480.9%
 
6.370.8%
 
Other values (227)58865.6%
 
ValueCountFrequency (%) 
0758.4%
 
0.110.1%
 
0.220.2%
 
0.320.2%
 
0.480.9%
 
0.550.6%
 
0.680.9%
 
0.780.9%
 
0.8121.3%
 
0.950.6%
 
ValueCountFrequency (%) 
47.510.1%
 
45.810.1%
 
45.710.1%
 
44.210.1%
 
43.310.1%
 
42.210.1%
 
40.410.1%
 
39.810.1%
 
39.310.1%
 
38.610.1%
 

Level 3 and 4#
Real number (ℝ≥0)

HIGH CORRELATION

Distinct251
Distinct (%)28.1%
Missing2
Missing (%)0.2%
Infinite0
Infinite (%)0.0%
Mean94.68568233
Minimum0
Maximum688
Zeros1
Zeros (%)0.1%
Memory size7.1 KiB
2020-12-12T16:53:47.073406image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile18
Q139.25
median67
Q3116
95-th percentile267.35
Maximum688
Range688
Interquartile range (IQR)76.75

Descriptive statistics

Standard deviation90.77084765
Coefficient of variation (CV)0.9586544176
Kurtosis8.518029656
Mean94.68568233
Median Absolute Deviation (MAD)33
Skewness2.579506578
Sum84649
Variance8239.346782
MonotocityNot monotonic
2020-12-12T16:53:47.152974image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%) 
38161.8%
 
37131.5%
 
41131.5%
 
52131.5%
 
76121.3%
 
31121.3%
 
39111.2%
 
43111.2%
 
28111.2%
 
49111.2%
 
42111.2%
 
60111.2%
 
27101.1%
 
46101.1%
 
40101.1%
 
80101.1%
 
50101.1%
 
6791.0%
 
4591.0%
 
3291.0%
 
6191.0%
 
4491.0%
 
3491.0%
 
4780.9%
 
4880.9%
 
Other values (226)62970.2%
 
ValueCountFrequency (%) 
010.1%
 
110.1%
 
510.1%
 
620.2%
 
760.7%
 
820.2%
 
910.1%
 
1010.1%
 
1160.7%
 
1250.6%
 
ValueCountFrequency (%) 
68810.1%
 
59810.1%
 
55410.1%
 
55210.1%
 
54110.1%
 
52410.1%
 
52110.1%
 
52010.1%
 
50510.1%
 
49510.1%
 

Level 3 and 4%
Real number (ℝ≥0)

HIGH CORRELATION

Distinct511
Distinct (%)57.2%
Missing2
Missing (%)0.2%
Infinite0
Infinite (%)0.0%
Mean50.47181208
Minimum0
Maximum97.9
Zeros1
Zeros (%)0.1%
Memory size7.1 KiB
2020-12-12T16:53:47.237547image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile14.625
Q135.75
median51.25
Q364.45
95-th percentile84.875
Maximum97.9
Range97.9
Interquartile range (IQR)28.7

Descriptive statistics

Standard deviation20.37568072
Coefficient of variation (CV)0.4037041643
Kurtosis-0.5110779366
Mean50.47181208
Median Absolute Deviation (MAD)14.35
Skewness-0.06970222123
Sum45121.8
Variance415.1683647
MonotocityNot monotonic
2020-12-12T16:53:47.319117image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%) 
50141.6%
 
66.780.9%
 
47.180.9%
 
59.470.8%
 
54.160.7%
 
54.560.7%
 
42.960.7%
 
33.350.6%
 
27.450.6%
 
56.440.4%
 
43.940.4%
 
41.940.4%
 
53.940.4%
 
73.640.4%
 
52.140.4%
 
62.940.4%
 
55.140.4%
 
6340.4%
 
48.340.4%
 
64.940.4%
 
42.740.4%
 
5940.4%
 
8040.4%
 
68.440.4%
 
30.340.4%
 
Other values (486)76585.4%
 
ValueCountFrequency (%) 
010.1%
 
0.910.1%
 
510.1%
 
6.110.1%
 
6.510.1%
 
6.930.3%
 
7.320.2%
 
7.410.1%
 
8.410.1%
 
9.230.3%
 
ValueCountFrequency (%) 
97.910.1%
 
97.710.1%
 
96.610.1%
 
95.210.1%
 
94.410.1%
 
94.310.1%
 
93.510.1%
 
93.310.1%
 
93.130.3%
 
92.910.1%
 

Interactions

2020-12-12T16:53:33.878551image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-12T16:53:33.952114image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-12T16:53:34.024176image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-12T16:53:34.102744image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-12T16:53:34.177308image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-12T16:53:34.246868image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-12T16:53:34.320932image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-12T16:53:34.390992image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-12T16:53:34.465556image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-12T16:53:34.538119image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-12T16:53:34.606678image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-12T16:53:34.675737image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-12T16:53:34.746798image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-12T16:53:34.819861image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-12T16:53:34.889921image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-12T16:53:34.962984image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-12T16:53:35.034546image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-12T16:53:35.104106image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-12T16:53:35.176168image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-12T16:53:35.243225image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-12T16:53:35.316288image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-12T16:53:35.386849image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-12T16:53:35.453907image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-12T16:53:35.523467image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-12T16:53:35.592526image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-12T16:53:35.668091image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-12T16:53:35.743156image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-12T16:53:35.822724image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-12T16:53:35.901292image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-12T16:53:35.974855image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-12T16:53:36.052922image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-12T16:53:36.130989image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-12T16:53:36.209557image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-12T16:53:36.285122image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-12T16:53:36.359186image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-12T16:53:36.433750image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-12T16:53:36.509315image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-12T16:53:36.583379image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-12T16:53:36.656942image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-12T16:53:36.734509image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-12T16:53:36.811575image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-12T16:53:36.884137image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-12T16:53:36.959702image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-12T16:53:37.030764image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-12T16:53:37.108330image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-12T16:53:37.182895image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-12T16:53:37.254456image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-12T16:53:37.326518image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-12T16:53:37.400081image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-12T16:53:37.467139image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-12T16:53:37.534197image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-12T16:53:37.604758image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-12T16:53:37.673817image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-12T16:53:37.737872image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-12T16:53:37.806431image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-12T16:53:37.872988image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-12T16:53:37.943049image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-12T16:53:38.010106image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-12T16:53:38.074162image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-12T16:53:38.140218image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-12T16:53:38.206776image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-12T16:53:38.280839image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-12T16:53:38.355404image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-12T16:53:38.432971image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-12T16:53:38.509536image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-12T16:53:38.581098image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-12T16:53:38.657664image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-12T16:53:38.728225image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-12T16:53:38.804790image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-12T16:53:38.881356image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-12T16:53:38.952918image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-12T16:53:39.024479image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-12T16:53:39.098043image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-12T16:53:39.165601image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-12T16:53:39.233158image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-12T16:53:39.304220image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-12T16:53:39.374280image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
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2020-12-12T16:53:40.446203image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
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2020-12-12T16:53:40.895089image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-12T16:53:40.966651image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-12T16:53:41.041715image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
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2020-12-12T16:53:44.093341image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

Correlations

2020-12-12T16:53:47.401688image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

Pearson's r

The Pearson's correlation coefficient (r) is a measure of linear correlation between two variables. It's value lies between -1 and +1, -1 indicating total negative linear correlation, 0 indicating no linear correlation and 1 indicating total positive linear correlation. Furthermore, r is invariant under separate changes in location and scale of the two variables, implying that for a linear function the angle to the x-axis does not affect r.

To calculate r for two variables X and Y, one divides the covariance of X and Y by the product of their standard deviations.
2020-12-12T16:53:47.543811image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

Spearman's ρ

The Spearman's rank correlation coefficient (ρ) is a measure of monotonic correlation between two variables, and is therefore better in catching nonlinear monotonic correlations than Pearson's r. It's value lies between -1 and +1, -1 indicating total negative monotonic correlation, 0 indicating no monotonic correlation and 1 indicating total positive monotonic correlation.

To calculate ρ for two variables X and Y, one divides the covariance of the rank variables of X and Y by the product of their standard deviations.
2020-12-12T16:53:47.685933image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

Kendall's τ

Similarly to Spearman's rank correlation coefficient, the Kendall rank correlation coefficient (τ) measures ordinal association between two variables. It's value lies between -1 and +1, -1 indicating total negative correlation, 0 indicating no correlation and 1 indicating total positive correlation.

To calculate τ for two variables X and Y, one determines the number of concordant and discordant pairs of observations. τ is given by the number of concordant pairs minus the discordant pairs divided by the total number of pairs.
2020-12-12T16:53:47.823551image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

Phik (φk)

Phik (φk) is a new and practical correlation coefficient that works consistently between categorical, ordinal and interval variables, captures non-linear dependency and reverts to the Pearson correlation coefficient in case of a bivariate normal input distribution. There is extensive documentation available here.

Missing values

2020-12-12T16:53:44.250477image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
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2020-12-12T16:53:44.590769image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-12T16:53:44.727887image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

Sample

First rows

NYC Results on the New York State 2006 Math Test (Grades 3-8)DBNGradeCategoryYearMean Scale ScoreStudents TestedLevel 1#Level 1%Level 2#Level 2%Level 3#Level 3%Level 4#Level 4%Level 3 and 4#Level 3 and 4%
0NaN07X0013All Students2006654.010418.017.325.024.052.050.09.08.761.058.7
1NaN07X0014All Students2006642.011125.022.540.036.041.036.95.04.546.041.4
2NaN07X0015All Students2006633.010931.028.441.037.635.032.12.01.837.033.9
3NaN07X001All GradesAll Students2006NaN32474.022.8106.032.7128.039.516.04.9144.044.4
4NaN07X0053All Students2006661.09612.012.520.020.851.053.113.013.564.066.7
5NaN07X0054All Students2006664.0849.010.713.015.555.065.57.08.362.073.8
6NaN07X0055All Students2006639.011325.022.141.036.343.038.14.03.547.041.6
7NaN07X005All GradesAll Students2006NaN29346.015.774.025.3149.050.924.08.2173.059.0
8NaN07X0183All Students2006656.08612.014.021.024.447.054.76.07.053.061.6
9NaN07X0184All Students2006655.0727.09.717.023.644.061.14.05.648.066.7

Last rows

NYC Results on the New York State 2006 Math Test (Grades 3-8)DBNGradeCategoryYearMean Scale ScoreStudents TestedLevel 1#Level 1%Level 2#Level 2%Level 3#Level 3%Level 4#Level 4%Level 3 and 4#Level 3 and 4%
886NaN12X3187All Students2006621.018455.029.991.049.538.020.70.00.038.020.7
887NaN12X3188All Students2006628.021255.025.9105.049.551.024.11.00.552.024.5
888NaN12X318All GradesAll Students2006NaN396110.027.8196.049.589.022.51.00.390.022.7
889NaN12X3416All Students2006646.08311.013.333.039.838.045.81.01.239.047.0
890NaN12X3417All Students2006638.07211.015.339.054.220.027.82.02.822.030.6
891NaN12X341All GradesAll Students2006NaN15522.014.272.046.558.037.43.01.961.039.4
892NaN12X6913All Students2006666.0161.06.33.018.89.056.33.018.812.075.0
893NaN12X6914All Students2006649.0192.010.510.052.65.026.32.010.57.036.8
894NaN12X6915All Students2006637.0163.018.88.050.05.031.30.00.05.031.3
895NaN12X691All GradesAll Students2006NaN516.011.821.041.219.037.35.09.824.047.1